International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Facial Recognition Based Criminal Alert System with Blockchain Secured Evidence Storage |
|---|---|
| ABSTRACT | Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, age-invariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations. |
| TITLE | |
| AUTHOR | DR. R. SRIDEVI, SATU SAI SANJANA YADAV Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India Post-Graduate Student, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407056 |
| pdf/56_Facial Recognition Based Criminal Alert System with Blockchain Secured Evidence.pdf | |
| KEYWORDS | |
| References | 1. T. J. Liu and C. C. Wang, "Face Aging Synthesis Using Deep Cycle GAN with Bias Loss," IEEE Transactions on Image Processing, vol. 32, 2023. 2. G. Singh, "Face Detection and Recognition System Using Digital Image Processing," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 13, no. 4, 2022. 3. C. R. Vishwanatha, M. K. Shreyas, et al., "Face Recognition and Identification Using Deep Learning," in Proceedings of the IEEE International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), 2023. 4. M. T. H. Fuad et al., "Recent Advances in Deep Learning Techniques for Face Recognition," IEEE Access, vol. 9, 2021. 5. M. Arsenovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "FaceTime: Deep Learning Based Face Recognition Attendance System," in Proceedings of the IEEE International Symposium on Intelligent Systems and Informatics (SISY), 2017. 6. M. H. Robin, M. M. U. Rahman, A. M. Taief, and Q. N. Eity, "Improvement of Face and Eye Detection Performance by Using Multi-task Cascaded Convolutional Networks," in Proceedings of the IEEE Region 10 Symposium (TENSYMP), 2020. 7. T. D. Tithy, S. Chakraborty, R. Islam, and A. Aziz, "A Deep Learning Based Approach for Real-Time Face Recognition System," in Proceedings of the IEEE International Conference on Electronics, Communications and Information Technology (ICECIT), 2021. 8. K. H. Teoh, R. C. Ismail, S. Z. M. Naziri, R. Hussin, M. N. M. Isa, and M. S. S. M. Basir, "Face Recognition and Identification Using Deep Learning Approach," Journal of Physics: Conference Series, 2021. 9. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, "Face Recognition: A Convolutional Neural Network Approach," IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98–113. 10. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, no. 4, pp. 399–458. 11. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks (MTCNN)," IEEE Signal Processing Letters, vol. 23, no. 10, 2016. 12. R. Sandhu and P. Singh, "Blockchain Technology for Secure Digital Evidence Management in Forensic Investigations," International Journal of Information Security, 2022. |
